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Context-Aware Intelligent User Interfaces for Supporting Sytem Context-Aware Intelligent User Interfaces for Supporting System Use Vom Fachbereich Informatik der Technischen Universität Darmstadt genehmigte Dissertation zur Erlangung des akademischen Grades Dr.-Ing. von Dipl.-Inform. Melanie Hartmann geboren in Seeheim-Jugenheim Referenten: Prof. Dr. Max Mühlhäuser (TU Darmstadt) Prof. Dr. Rainer Malaka (Universität Bremen) Tag der Einreichung: 4. Dezember 2009 Tag der mündlichen Prüfung: 18. Januar 2010 Darmstadt 2010 Hochschulkennziffer D17 i Ehrenwörtliche Erklärung1 Hiermit erkläre ich, die vorgelegte Arbeit zur Erlangung des akademischen Grades “Dr.-Ing.” mit dem Titel “Context-Aware Intelligent User Interfaces for Support- ing System Use” selbständig und ausschließlich unter Verwendung der angegebenen Hilfsmittel erstellt zu haben. Ich habe bisher noch keinen Promotionsversuch unter- nommen. Darmstadt, den 4. Dezember 2009 Melanie Hartmann 1Gemäß §9 Abs. 1 der Promotionsordnung der TU Darmstadt Abstract Two trends can be observed in current applications: the amount of functionality of- fered is constantly increasing and applications are more often used in mobile settings. Both trends often lead to a decrease in the usability of applications. This effect can be countered by Intelligent User Interfaces that facilitate the interaction between user and application. In order to support the user in an optimal way, the Intelligent User Interface has to be aware of the user’s needs and adapt the provided support to her current situation, i.e. her current context. The relevant context information can thereby be gathered from the environment (e.g. her current location) or from sophisticated user models that reflect the user’s behavior. In this thesis, we present a novel approach of combining user and environmental context information for supporting the system use. As context information is often very error-prone and the user‘s workflow should not be disrupted by erroneous in- teraction support, we adapt the presentation of the support to the reliability of the context information. Therefore, we use different levels of proactivity from unobtru- sive highlighting to automatically performing tasks. The presented approach –called AUGUR– is application independent and is able to support the interaction for arbi- trary existing applications, even across application boundaries. For that purpose, we developed a novel application modeling language that is able to model applications and their relationships to context. The application models can thereby be defined by the application developer, learned by AUGUR, and augmented by the end-user. The interaction can be facilitated by an Intelligent User Interface in two different ways: on the one hand by supporting the entering of data and on the other hand by simplifying the navigation within and between applications. For supporting the user in entering data, we contribute three approaches based on (i) the user’s previous interactions, (ii) the information represented in the application models, and (iii) the semantics of the data required by the user interface and the context information currently relevant for the user. For the latter, we developed a novel algorithm that combines string-based and semantic similarity measures. AUGUR supports the user’s navigation in three different ways: It can (i) guide the user through an application, (ii) provide navigation shortcuts to other applications, and (iii) reduce the user interface to the most relevant functionality for mobile use. iv For guiding the user, we developed a novel algorithm called FxL that is able to predict the next relevant interaction element. For the interface adaptation, we introduce a novel approach based on FxL to determine the elements that should be presented to the user for mobile use according to her current situation. We realized the developed concepts of AUGUR in a working prototype. Further, we evaluated the usability of the context-aware support provided by AUGUR in a user study, and showed that it can significantly increase the usability of an application. Zusammenfassung Heutige Anwendungen werden zum einen immer komplexer und zum anderen zu- nehmend auf mobilen Endgeräten verwendet. Beide Faktoren beeinträchtigen häufig die Gebrauchstauglichkeit der Anwendungen. Intelligente Benutzungsschnittstellen wirken dem entgegen indem sie den Benutzer bei der Interaktion mit einer Anwen- dung unterstützen. Um die optimale Unterstützung bieten zu können, muss sich die intelligente Benutzungsschnittstelle an die Bedürfnisse des Benutzers und an seine aktuelle Situation, d.h. seinen Kontext, anpassen. Kontextinformationen können dabei aus der Umgebung des Benutzers gewonnen werden (z.B. sein aktueller Aufen- thaltsort) oder aus Benutzermodellen, die das Verhalten des Benutzers widerspiegeln. In dieser Arbeit präsentieren wir einen neuen Ansatz zur kontextsensitiven In- teraktionsunterstützung, der sowohl den Umgebungs- als auch den Benutzerkontext berücksichtigt. Da Kontextinformationen häufig fehlerbehaftet sind und der Ar- beitsfluss des Benutzers nicht mit fehlerhafter Unterstützung gestört werden soll, müssen wir die Zuverlässigkeit der genutzten Kontextdaten bei der Interaktionsun- terstützung berücksichtigen. Daher passen wir die Darstellung der Interaktionsunter- stützung an die Zuverlässigkeit der zugrunde liegenden Daten an. Die Darstellung reicht von unaufdringlichem Hervorheben relevanter Elemente bis zur automatis- chen Ausführung von Anwendungsschritten. Der vorgestellte Ansatz zur kontextab- hängigem Interaktionsunterstützung, genannt AUGUR, ist anwendungsunabhängig und kann den Benutzer auch über Anwendungsgrenzen hinweg unterstützen. Dies wird durch eine neue Anwendungsmodelierungssprache ermöglicht, die uns erlaubt, Anwendungen und ihren Zusammenhang zu Kontextinformationen zu modellieren. Die Anwendungsmodelle können dabei vom Anwendungsentwickler erstellt werden oder von AUGUR erlernt werden. Zusätzlich können sie jederzeit vom Endbenutzer überprüft und erweitert werden. Wir konzentrieren uns in der vorliegenden Arbeit auf die Unterstützung des Be- nutzers bei der Dateneingabe und bei der Navigation in und zwischen Anwendun- gen. Zur Unterstützung bei der Dateneingabe verwenden wir folgende Informa- tionsquellen: (i) die bisherigen Interaktionen des Benutzers, (ii) das zugehörige An- wendungsmodell und (iii) die Semantik der von der Anwendung benötigten Daten und des aktuellen Kontexts des Benutzers. Für Letzteres haben wir einen neuen Al- vi gorithmus entwickelt, der zeichenfolgenbasierte und semantische Ähnlichkeitsmasse kombiniert und neue Zusammenhänge erlernt. Die Navigation des Benutzers wird von AUGUR auf drei verschiedene Arten unterstützt: AUGUR kann (i) den Benutzer durch eine Anwendung führen, (ii) Nav- igationsshortcuts zu anderen Anwendungen vorschlagen und (iii) automatisch eine reduzierte Version der Anwendungsoberfläche für mobile Benutzung generieren. Um den Benutzer durch eine Anwendung zu führen, haben wir einen neuen Algorithmus namens FxL entwickelt, der in der Lage ist, das nächste relevante Interaktionsele- ment auf Basis der bisherigen Interaktionen vorherzusagen. Für die Generierung der reduzierten Benutzungsoberfläche stellen wir einen neuen Algorithmus basierend auf FxL vor, der die Interaktionselemente bestimmen kann, die idealerweise dem Benutzer angezeigt werden sollten. Die entwickelten Konzepte wurden in einem Prototyp umgesetzt und getestet. Darüberhinaus haben wir die Gebrauchstauglichkeit der kontextabhängigen Inter- aktionsunterstützung in AUGUR in einer Benutzerstudie überprüft. Wir konnten zeigen, dass eine solche Unterstützung die Gebrauchstauglichkeit einer Anwendung signifikant erhöhen kann. Acknowledgments This work would not have been possible without the continuous support and encour- agement of my colleagues, family and friends over the last years, which I would like to acknowledge here. First and foremost, I would like to thank my advisor, Max Mühlhäuser, for his unlimited support, excellent advice and faith in my work. I am also grateful to Rainer Malaka (Universität Bremen) for acting as second referee. I am grateful to all at Telecooperation and RBG for providing me with a friendly and supportive place to work. Especially, I would like to thank Daniel Schreiber for all the fruitful discussions and support (It is really a pleasure working with you). I am also grateful to all the other members in the Telecooperation Group who proof- read papers and provided feedback (Andreas Behring, Dirk Schnelle-Walka, Felix Flentge, Tobias Klug, Sebastian Ries, Jürgen Steimle to name only few of them). I would also like to thank Manuel Görtz and Andreas Faatz from SAP research for their support in the AUGUR project and all the others from SAP we cooperated with. Furthermore, I would also like to acknowledge Holger Ziekow (HU Berlin), Dominikus Heckmann (DFKI), Frederik Janssen (TUD), Anthony Jameson (DFKI), and Christine Müller (Jacobs University Bremen) for their advice. Many thanks are also due to Marcus Ständer, Matthias Beckerle and Markus Miche for their support and for continuously supplying me with chocolate. I am highly grateful to my brother and parents for their support and patience during the course of this work. Finally, many thanks are due to Torsten Zesch for his unlimited support and for standing all the stressful time with me when finishing the thesis. Contents 1. Introduction 1 1.1. AUGUR .................................. 4 1.2. Main Contributions ............................ 6 1.3. Publication Record ...........................
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